A framework model to predict equipment failure has been keenly sought by Asset intensive organizations.

Timely prediction of equipment failure not only reduces direct and indirect costs by avoiding a complete equipment breakdown but also reduces unexpected shut-downs, accident, and unwarranted emission risk.

We performed an experiment by applying data extraction algorithm on equipment maintenance records residing in SAP application that consisted of condition monitoring measurements, spare part usage, elapsed time period since the last completed maintenance and the next closest preventive maintenance order scheduled in a future date.

Data was collected from SAP Internet Demonstration and Evaluation System (IDES) application for equipment of type pumps.The database prepared for model consisted of 274 instances
for 39 equipments and each instance was associated with 11 features. Each instance in the data model was classified either as preventive, corrective or breakdown based on origin of the maintenance process that generated the maintenance order for the instance. This data was split into 75 percent into
the training set (n=205) and 25 percent into test set (n=52)

We applied unsupervised learning technique of clustering and performed classes to cluster evaluation with 80 percent accuracy. As part of supervised learning, data from the finalized data model was fed into various Machine Learning (ML) algorithms where the classifier model was trained to predict preventive, corrective breakdown scenarios, and subsequently tested on mutually exclusive data sets.

The Support vector machine (SVM) and Decision Tree (DT) algorithms were able to classify and therefore, predict equipment breakdown with high accuracy and true positive rate (TPR) of more than 95 percent. SAP and Machine Learning integration has huge potential to be realized